Hi I am using boosting for a classification and prediction problem. For some reason it is giving me an outcome that doesn't fall between 0 and 1 for the predictions. I have tried type="response" but it made no difference. Can anyone see what I am doing wrong? Screen output shown below:> boost.model <- gbm(as.factor(train$simNuance) ~ ., # formula+ data=train, # dataset + # +1: monotone increase, + # 0: no monotone restrictions + distribution="gaussian", # bernoulli, adaboost, gaussian, + # poisson, and coxph available + n.trees=3000, # number of trees + shrinkage=0.005, # shrinkage or learning rate, + # 0.001 to 0.1 usually work + interaction.depth=3, # 1: additive model, 2: two-way interactions, etc. + bag.fraction = 0.5, # subsampling fraction, 0.5 is probably best + train.fraction = 0.5, # fraction of data for training, + # first train.fraction*N used for training + n.minobsinnode = 10, # minimum total weight needed in each node + cv.folds = 5, # do 5-fold cross-validation + keep.data=TRUE, # keep a copy of the dataset with the object + verbose=FALSE) # print out progress> > best.iter = gbm.perf(boost.model,method="cv") > pred = predict.gbm(boost.model, test, best.iter) > summary(pred)Min. 1st Qu. Median Mean 3rd Qu. Max. 0.4772 1.5140 1.6760 1.5100 1.7190 1.9420 -------------- next part -------------- Checked by AVG Free Edition.
Perhaps by following the Posting Guide you're likely to get more helpful responses. You have not shown an example that others can reproduce, not given version information for R or gbm. The output you showed does not use type="response", either. Andy _____ From: r-help-bounces at stat.math.ethz.ch on behalf of stephenc Sent: Sat 5/27/2006 4:02 PM To: 'R Help' Subject: [R] boosting - second posting [Broadcast] Hi I am using boosting for a classification and prediction problem. For some reason it is giving me an outcome that doesn't fall between 0 and 1 for the predictions. I have tried type="response" but it made no difference. Can anyone see what I am doing wrong? Screen output shown below:> boost.model <- gbm(as.factor(train$simNuance) ~ ., # formula+ data=train, # dataset + # +1: monotone increase, + # 0: no monotone restrictions + distribution="gaussian", # bernoulli, adaboost, gaussian, + # poisson, and coxph available + n.trees=3000, # number of trees + shrinkage=0.005, # shrinkage or learning rate, + # 0.001 to 0.1 usually work + interaction.depth=3, # 1: additive model, 2: two-way interactions, etc. + bag.fraction = 0.5, # subsampling fraction, 0.5 is probably best + train.fraction = 0.5, # fraction of data for training, + # first train.fraction*N used for training + n.minobsinnode = 10, # minimum total weight needed in each node + cv.folds = 5, # do 5-fold cross-validation + keep.data=TRUE, # keep a copy of the dataset with the object + verbose=FALSE) # print out progress> > best.iter = gbm.perf(boost.model,method="cv") > pred = predict.gbm(boost.model, test, best.iter) > summary(pred)Min. 1st Qu. Median Mean 3rd Qu. Max. 0.4772 1.5140 1.6760 1.5100 1.7190 1.9420
The family arg appears to be the problem. Either bernoulli or adaboost are appropriate for classification problems. Max> Perhaps by following the Posting Guide you're likely to get morehelpful> responses. You have not shown an example that others can reproduce,not> given version information for R or gbm. The output you showed doesnot use> type="response", either. > > Andy > > _____ > > From: r-help-bounces at stat.math.ethz.ch on behalf of stephenc > Sent: Sat 5/27/2006 4:02 PM > To: 'R Help' > Subject: [R] boosting - second posting [Broadcast] > > > > Hi > > I am using boosting for a classification and prediction problem. > > For some reason it is giving me an outcome that doesn't fall between 0> and 1 for the predictions. I have tried type="response" but it madeno> difference. > > Can anyone see what I am doing wrong? > > Screen output shown below: > > > > boost.model <- gbm(as.factor(train$simNuance) ~ ., # formula> + data=train, # dataset > + # +1: monotone increase, > + # 0: no monotone restrictions> + distribution="gaussian", # bernoulli, adaboost,gaussian,> + # poisson, and coxph available> + n.trees=3000, # number of trees > + shrinkage=0.005, # shrinkage or learning rate, > + # 0.001 to 0.1 usually work > + interaction.depth=3, # 1: additive model, 2:two-way> interactions, etc. > + bag.fraction = 0.5, # subsampling fraction, 0.5 is> probably best > + train.fraction = 0.5, # fraction of data fortraining,> + # first train.fraction*N used > for training > + n.minobsinnode = 10, # minimum total weight neededin> each node > + cv.folds = 5, # do 5-fold cross-validation > + keep.data=TRUE, # keep a copy of the dataset > with the object > + verbose=FALSE) # print out progress > > > > best.iter = gbm.perf(boost.model,method="cv") > > pred = predict.gbm(boost.model, test, best.iter) > > summary(pred) > Min. 1st Qu. Median Mean 3rd Qu. Max.> 0.4772 1.5140 1.6760 1.5100 1.7190 1.9420 ---------------------------------------------------------------------- LEGAL NOTICE\ Unless expressly stated otherwise, this messag...{{dropped}}